- [Instructor] So we look at VGG16,…which is the model created by the Visual Geometry Group…at Oxford University,…which won the 2014 ImageNet Competition,…as it's one of the simpler models to understand.…The VGG ImageNet team created both a larger, slower,…and slightly more accurate model, VGG19,…and a smaller, faster model, VGG16.…Remember that as the winner of an ImageNet competition,…the model would have to recognize 1000 categories of images.…So let's head over to Keras to look at VGG…in a little more detail.…

Fortunately for us, VGG16 comes with Keras.…What we're going to do is use a world-class model…and look at the steps involved in recognizing…a random object.…And we will see how well the VGG16 model manages this.…So we import the relevant libraries from Keras.…If this is the first time…that you're going to be using the VGG16 model,…the weights have to be downloaded…from the author's GitHub site.…The reason we do this is because instead of having to create…and train VGG16 on the ImageNet dataset from scratch,…

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5/4/2018

Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this hands-on course, instructor Jonathan Fernandes covers fundamental neural and convolutional neural network concepts. Jonathan begins by providing an introduction to the components of neural networks, discussing activation functions and backpropagation. He then looks at convolutional neural networks, explaining why they're particularly good at image recognition tasks. He also steps through how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.